NNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry

Neural network quantum state (NNQS) has emerged as a promising candidate for quantum many-body problems, but its practical applications are often hindered by the high cost of sampling and local energy calculation. We develop a high-performance NNQS method for \textit{ab initio} electronic structure calculations. The major innovations include: (1) A transformer based architecture as the quantum wave function ansatz; (2) A data-centric parallelization scheme for the variational Monte Carlo (VMC) algorithm which preserves data locality and well adapts for different computing architectures; (3) A parallel batch sampling strategy which reduces the sampling cost and achieves good load balance; (4) A parallel local energy evaluation scheme which is both memory and computationally efficient; (5) Study of real chemical systems demonstrates both the superior accuracy of our method compared to state-of-the-art and the strong and weak scalability for large molecular systems with up to $120$ spin orbitals.Comment: Accepted by SC'2

Generating bright-field images of stained tissue slices from Mueller matrix polarimetric images with CycleGAN using unpaired dataset

Recently, Mueller matrix (MM) polarimetric imaging-assisted pathology detection methods are showing great potential in clinical diagnosis. However, since our human eyes cannot observe polarized light directly, it raises a notable challenge for interpreting the measurement results by pathologists who have limited familiarity with polarization images. One feasible approach is to combine MM polarimetric imaging with virtual staining techniques to generate standardized stained images, inheriting the advantages of information-abundant MM polarimetric imaging. In this study, we develop a model using unpaired MM polarimetric images and bright-¯eld images for generating standard hematoxylin and eosin (H&E) stained tissue images. Compared with the existing polarization virtual staining techniques primarily based on the model training with paired images, the proposed Cycle-Consistent Generative Adversarial Networks (CycleGAN)based model simpli¯es data acquisition and data preprocessing to a great extent. The outcomes demonstrate the feasibility of training CycleGAN with unpaired polarization images and their corresponding bright-¯eld images as a viable approach, which provides an intuitive manner for pathologists for future polarization-assisted digital pathology

Differentiable matrix product states for simulating variational quantum computational chemistry

Quantum Computing is believed to be the ultimate solution for quantum chemistry problems. Before the advent of large-scale, fully fault-tolerant quantum computers, the variational quantum eigensolver (VQE) is a promising heuristic quantum algorithm to solve real world quantum chemistry problems on near-term noisy quantum computers. Here we propose a highly parallelizable classical simulator for VQE based on the matrix product state representation of quantum state, which significantly extend the simulation range of the existing simulators. Our simulator seamlessly integrates the quantum circuit evolution into the classical auto-differentiation framework, thus the gradients could be computed efficiently similar to the classical deep neural network, with a scaling that is independent of the number of variational parameters. As applications, we use our simulator to study commonly used small molecules such as HF, HCl, LiH and H$_2$O, as well as larger molecules CO$_2$, BeH$_2$ and H$_4$ with up to $40$ qubits. The favorable scaling of our simulator against the number of qubits and the number of parameters could make it an ideal testing ground for near-term quantum algorithms and a perfect benchmarking baseline for oncoming large scale VQE experiments on noisy quantum computers

Understanding Transport of an Elastic, Spherical Particle through a Confining Channel

The transport of soft particles through narrow channels or pores is ubiquitous in biological systems and industrial processes. On many occasions, the particles deform and temporarily block the channel, inducing a built-up pressure. This pressure buildup often has a profound effect on the behavior of the respective system; yet, it is difficult to be characterized. In this work, we establish a quantitative correlation between the built-up pressure and the material and geometry properties through experiments and mechanics analysis. We fabricate microgels with a controlled diameter and elastic modulus by microfluidics. We then force them to individually pass through a constrictive or straight confining channel and monitor the pressure variation across the channel. To interpret the pressure measurement, we develop an analytical model based on the Neo-Hookean material law to quantify the dependence of the maximum built-up pressure on the radius ratio of the elastic sphere to the channel, the elastic modulus of the sphere, and two constant parameters in the friction constitutive law between the sphere and the channel wall. This model not only agrees very well with the experimental measurement conducted at large microgel deformation but also recovers the classical theory of contact at small deformation. Featuring a balance between accuracy and simplicity, our result could shed light on understanding various biological and engineering processes involving the passage of elastic particles through narrow channels or pores

A dual-modality imaging method based on polarimetry and second harmonic generation for characterization and evaluation of skin tissue structures

The characterization and evaluation of skin tissue structures are crucial for dermatological applications. Recently, Mueller matrix polarimetry and second harmonic generation microscopy have been widely used in skin tissue imaging due to their unique advantages. However, the features of layered skin tissue structures are too complicated to use a single imaging modality for achieving a comprehensive evaluation. In this study, we propose a dual-modality imaging method combining Mueller matrix polarimetry and second harmonic generation microscopy for quantitative characterization of skin tissue structures. It is demonstrated that the dual-modality method can well divide the mouse tail skin tissue specimens' images into three layers of stratum corneum, epidermis, and dermis. Then, to quantitatively analyze the structural features of different skin layers, the gray level co-occurrence matrix is adopted to provide various evaluating parameters after the image segmentations. Finally, to quantitatively measure the structural differences between damaged and normal skin areas, an index named Q-Health is defined based on cosine similarity and the gray-level co-occurrence matrix parameters of imaging results. The experiments confirm the effectiveness of the dual-modality imaging parameters for skin tissue structure discrimination and assessment. It shows the potential of the proposed method for dermatological practices and lays the foundation for further, in-depth evaluation of the health status of human skin

A quantitative technique to analyze and evaluate microstructures of skin hair follicles based on mueller matrix polarimetry

In this study, we propose a quantitative technique to analyze and evaluate microstructures of skin hair follicles based on Mueller Matrix transmission microscopy. We measure the Mueller matrix polar decomposition (MMPD) parameter images to reveal the characteristic linear birefringence distribution induced by hair follicles in mouse skin tissue samples. The results indicate that the Mueller matrix-derived parameters can be used to reveal the location and structural integrity of hair follicles. For accurate hair follicle location identification and quantitative structural evaluations, we use the image segmentation method, sliding window algorithm, and image texture analysis methods together to process the Mueller matrix-derived images. It is demonstrated that the hair follicle regions can be more accurately recognized, and their locations can be precisely identified based on the Mueller matrix-derived texture parameters. Moreover, comparisons between manual size measurement and polarimetric calculation results confirm that the Mueller matrix parameters have good performance for follicle size estimation. The results shown in this study suggest that the technique based on Mueller matrix microscopy can realize automatically hair follicle identification, detection, and quantitative evaluation. It has great potential in skin structure-related studies and clinical dermatological applications

Improved image reconstruction based on ultrasonic transmitted wave computerized tomography on concrete

Abstract Considering the non-uniqueness and instability of concrete defect detection in construction engineering, ultrasonic time of flight data and maximum likelihood expectation maximization algorithm were proposed to improve the readability of the ultrasonic reconstruction image. Ultrasonic transmitted waves reflect internal information when through concrete structures. Measurement signals were regularized and processed as transit time parameters. Maximum likelihood expectation maximization algorithm was optimized for image reconstruction of the concrete structure. The interpolation data as the density of measurement data was used to improve the readability of reconstruction images. The experimental measurement system, improved image reconstruction algorithm, and interpolation method were verified in detail by using simulation and concrete phantoms. The proposed image reconstruction technique based on maximum likelihood expectation maximization overcame the disadvantages of the traditional image reconstruction method, and it was effective to improve the accuracy and the quality of image reconstruction on concrete. Time of flight data and normalized interpolation were discussed in detail during image reconstruction. The results showed that it could make the position of an abnormal object more obvious, and it was more accurate for reflecting the internal structure of concrete

Preservation of image edge feature based on snowfall model smoothing filter

Abstract This paper proposed a snowfall model as a novel smoothing filter. The pixel composition of the image was similar to the geographic features, so it could be smooth because of snow accumulation. In the snowfall processing, luminance changes are linked to terrain and snowfall amount. Curvature and luminance gradient decided the amount of snowfall; the amount of snowfall became large on the parts where the curvature was large, and it became little on the parts where the gradient was steep. Snowfall algorithm simulates the natural snowfall process, which nonlinear diffusion and the target feature could be preserved well. Snowfall model has the same function as the Gaussian filter. The number of regions was reduced after Gaussian filter and snowfall model smoothing, respectively. The contrast experiment was carried out based on Watershed algorithm. The image area segmentation that pretreated through snowfall model was compared with Gaussian filter smoothing. The experimental result showed that the proposed snowfall model was a smoothing filter. It was able to realize edge preservation, which was the original purpose, and it was also possible to apply to region segmentation